Photonic integrated circuits are facilitating the development of optical neural networks, which have the potential to be both faster and more energy efficient than their electronic counterparts since optical signals are especially well-suited for implementing matrix multiplications. However, accurate programming of photonic chips for optical matrix multiplication remains a difficult challenge. Here, we describe both simple analytical models and data-driven models for offline training of optical matrix multipliers. We train and evaluate the models using experimental data obtained from a fabricated chip featuring a Mach-Zehnder interferometer mesh implementing 3-by-3 matrix multiplication. The neural network-based models outperform the simple physics-based models in terms of prediction error. Furthermore, the neural network models are also able to predict the spectral variations in the matrix weights for up to 100 frequency channels covering the C-band. The use of neural network models for programming the chip for optical matrix multiplication yields increased performance on multiple machine learning tasks.

翻译：光子集成电路正在推动光学神经网络的发展，由于光信号尤其适用于实现矩阵乘法，这类网络相比电子神经网络具有潜在的更快速度和更高能效优势。然而，精确编程光子芯片以实现光学矩阵乘法仍是一项艰巨挑战。本文描述了适用于光学矩阵乘法器离线训练的简易解析模型与数据驱动模型。我们利用从制造芯片获得的实验数据（该芯片包含实现3×3矩阵乘法的马赫-曾德尔干涉仪网格）对模型进行训练与评估。基于神经网络的模型在预测误差方面优于基于物理的简易模型。此外，神经网络模型还能预测覆盖C波段的100个频率通道中矩阵权重的光谱变化。将神经网络模型用于编程芯片实现光学矩阵乘法，可在多个机器学习任务上获得更优性能。